I have GPS data collected with a smartphone app for all travel modes (e.g., car, bike, walking, transit). Trips were detected by the app (so I have trip_id in the data). In theory, each point is collected every 5 seconds; it varies in reality (5-15 seconds). The accuracy is pretty low (from 4 to around 70, some points are completely off with accuracy of ~150). This accuracy was measured in meters, I think 4 means that the point has 68% chance to fall inside a buffer of 4m.
My goals are to clean this dataset and use it to determine speed, travel distance, and evaluate the routes. With that, I'll snap it into the road network (with a map matching tool such as nearest_osm in R or similar).
What accuracy threshold should I use to filter out "bad" points before I can snap these points to the network?